Senior Engineer, Model Serving
Databricks11 months ago
Base Salary
$166k - $225k/yr
Responsibilities
- Design and implement scalable, reliable core systems and APIs for Databricks Model Serving.
- Drive architectural decisions and optimize performance, throughput, autoscaling, and operational efficiency for CPU and GPU workloads.
- Develop serving infrastructure components including model container builds, deployment workflows, routing, caching, observability, and intelligent autoscaling.
- Collaborate with product, platform, infrastructure, and research teams to deliver reliable and performant systems.
- Lead initiatives improving latency, availability, and cost-effectiveness across serving layers.
- Establish practices for code quality, testing, and operational readiness, and mentor engineers through design reviews and technical guidance.
Requirements
- At least 5 years of experience building and operating large-scale distributed systems.
- Experience with model serving, inference systems, or related infrastructure such as routing, scheduling, autoscaling, and observability.
- Strong foundation in algorithms, data structures, and system design for large-scale, low-latency serving systems.
- Experience architecting large-scale, performance-sensitive CPU/GPU inference systems.
- Demonstrated ability to deliver technically complex, high-impact initiatives with measurable customer or business value.
- Strong communication and cross-functional collaboration skills.
- Customer-focused mindset and ability to align implementation details with product goals.
- Interest in mentoring engineers and fostering technical excellence.
Benefits
- Comprehensive employee benefits and perks, with region-specific details available through Databricks' benefits portal.
Tech Stack
Apache SparkMLflow
Categories
About Databricks
Databricks builds a cloud-based data and AI platform centered on the lakehouse architecture, combining data engineering, analytics, and machine learning with Apache Spark, Delta Lake, and MLflow. It sells subscriptions and cloud services to enterprises that need to unify data pipelines and develop large-scale AI and analytics. Founded in 2013 by the creators of Apache Spark and headquartered in San Francisco, the company is privately held and serves organizations across many industries.